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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Causal-JEPA: Learning World Models through Object-Level Latent Masking · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2602.11389/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2602.11389/feed.xml" rel="self" type="application/rss+xml" /><item><title>Causal-JEPA: Learning World Models through Object-Level Latent Masking — mentioned by HarHarVeryFunny</title><link>https://news.ycombinator.com/item?id=48554054</link><guid isPermaLink="false">urn:hackerpapers:mention:2602.11389:48554054</guid><pubDate>Tue, 16 Jun 2026 12:16:48 GMT</pubDate><description>&lt;p&gt;As the saying goes: correlation is not causation OTOH prediction doesn&amp;#x27;t necessarily reflect causation either, but prediction is what JEPA is about, how our brain/intelligence works, and one of the great confirmations of LLMs is how powerful prediction errors are as a learning signal. JEPA appears a step in the right direction of trying to build a brain rather than a language model - to use prediction the way the brain uses it to predict the future (not an historical frozen training set), and learn a real world model of how the world behaves. Any JEPA implementations I&amp;#x27;ve read about use a…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2602.11389/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
